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Computational Brain & Behavior is a peer-reviewed journal published by Springer Nature, covering Neural and Behavioral Psychology Studies, Decision-Making and Behavioral Economics, Neural dynamics and brain function.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. Computational Brain & Behavior 12, 45–58 (2023).
Formats any DOI in Computational Brain & Behavior style. No sign-up. |
| Publishes research in | Neural and Behavioral Psychology Studies Decision-Making and Behavioral Economics Neural dynamics and brain function Mental Health Research Topics Philosophy and History of Science |
| ISSN | 2522-0861 |
| Citation impact (2-yr) | 2.43 |
| h-index | 30 |
| i10-index | 97 |
| Total citations | 3,540 |
| Article processing charge | $2,990 |
| Top institutions publishing here | University of Amsterdam |
| Journal website | www.springer.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Cross-validation (CV) is increasingly popular as a generic method to adjudicate between mathematical models of cognition and behavior. In order to measure model generalizability, CV quantifies out-of-sample predictive performance, and the CV preference goes to the model that predicted the out-of-sample data best. The advantages of CV include theoretic simplicity and practical feasibility. Despite its…
In an earlier article in this journal, Gronau and Wagenmakers (2018) discuss some problems with leave-one-out cross-validation (LOO) for Bayesian model selection. However, the variant of LOO that Gronau and Wagenmakers discuss is at odds with a long literature on how to use LOO well. In this discussion, we discuss the use of LOO in…
Abstract In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such reasoning is to generalize claims about the model in question, to claims about the mind and brain, and the neurocognitive capacities of those systems. Such inference…
